Multi-agent Coordination and Optimal Dispatch of Microgrid with CES Based on Ecological Game

Xianshan LI; Aobo CHEN; Shan CHENG; Minrui CHEN

doi:10.11930/j.issn.1004-9649.202005004

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Multi-agent Coordination and Optimal Dispatch of Microgrid with CES Based on Ecological Game

New Energy | 更新时间：2025-06-18

- Multi-agent Coordination and Optimal Dispatch of Microgrid with CES Based on Ecological Game
- Electric Power Vol. 54, Issue 7, Pages: 166-177(2021)
- 作者机构：
  
  梯级水电站运行与控制湖北省重点实验室（三峡大学），湖北宜昌　443002
- 作者简介：
- 基金信息：
  
  This work is supported by the National Natural Science Foundation of China (No.51607105) and the Natural Science Foundation of Hubei Province (No.2016CFA097)
- DOI：10.11930/j.issn.1004-9649.202005004
  CLC：
- Received：06 May 2020，
  
  Revised：2020-10-20，
  
  Published：05 July 2021
- 稿件说明：
移动端阅览
Xianshan LI, Aobo CHEN, Shan CHENG, et al. Multi-agent Coordination and Optimal Dispatch of Microgrid with CES Based on Ecological Game[J]. Electric power, 2021, 54(7): 166-177.
DOI：

Xianshan LI, Aobo CHEN, Shan CHENG, et al. Multi-agent Coordination and Optimal Dispatch of Microgrid with CES Based on Ecological Game[J]. Electric power, 2021, 54(7): 166-177. DOI： 10.11930/j.issn.1004-9649.202005004.

摘要

分布式储能可以缓解分布式电源大量接入微电网所带来的随机性问题，但高昂的初装成本和运维困难限制了其大规模推广应用。在微电网中引入“云储能”为用户提供高效的“虚拟分布式储能”服务，基于自然界生态系统思想，提出了含云储能微电网多智能体生态博弈协调优化调度模型。根据利益诉求关系，构建了微电网系统多智能体结构，得到微电网运营商、常规负荷代理、云储能运营商以及云储能用户四大智能体，建立了其优化模型；构建了微电网电力生态系统，建立了各智能体之间以及电力生态系统之间的博弈优化模型；采用基于纳什均衡的强化学习算法对多智能体生态博弈模型进行求解。算例结果表明，云储能服务优化了负荷曲线、降低了用电成本、云储能运营商也获得了收益，达到多方共赢效果。

Abstract

Distributed energy storage can alleviate the randomness problem caused by a large number of distributed power sources connected to micro-grid

but high initial installation cost and operation and maintenance difficulties limit its large-scale promotion and application. In this paper

“cloud energy storage” system is introduced to micro-grid to provide users with efficient “virtual distributed energy storage” services. Based on the idea of natural ecosystems

a multi-agent ecological game coordination optimization dispatching model for microgrids with CES is proposed. According to interest appealing relationship

the multi-agent structure of the microgrid system is constructed with four intelligent agents

including micro-grid operator

general load aggregators

cloud energy storage and cloud storage users

and their optimization models were developed respectively. The micro-grid power ecosystem was constructed

and the game optimization model among agents and among power ecosystems was established. The reinforcement learning algorithm based on the Nash equilibrium was used to solve the multi-agent ecological game model. The simulation results show that the cloud energy storage service optimizes the load curve

reduces the electricity cost

and cloud energy storage operators also gain benefits

achieving a multi-party win-win effect.

关键词

Keywords

references

赵伟, 肖祥, 郑耀东, 等. 分布式储能网络化运营平台架构与控制策略[J]. 高电压技术, 2019, 45(10): 3256–3262.

ZHAO Wei, XIAO Xiang, ZHENG Yaodong, et al . Architecture and control strategy of distributed energy storage networked operation platform[J ] . High Voltage Technology, 2019, 45(10): 3256–3262.

李建林, 马会萌, 袁晓冬, 等. 规模化分布式储能的关键应用技术研究综述[J]. 电网技术, 2017, 41(10): 3365–3375.

LI Jianlin, MA Huimeng, YUAN Xiaodong, et al . Overview on key applied technologies of large-scale distributed energy storage[J ] . Power System Technology, 2017, 41(10): 3365–3375.

孙偲, 陈来军, 邱欣杰, 等. 基于合作博弈的发电侧共享储能规划模型[J]. 全球能源互联网, 2019, 2(4): 360–366.

SUN Cai, CHEN Laijun, QIU Xinjie, et al . Co-generation game-based shared energy storage planning model for power generation[J ] . Global Energy Internet, 2019, 2(4): 360–366.

陶琼, 桑丙玉, 叶季蕾, 等. 高光伏渗透率配电网中分布式储能系统的优化配置方法[J]. 高电压技术, 2016, 42(7): 2158–2165.

TAO Qiong, SANG Bingyu, YE Jilei, et al . Optimal configuration method of distributed energy storage systems in distribution network with high penetration of photovoltaic[J ] . High Voltage Technology, 2016, 42(7): 2158–2165.

张自东, 邱才明, 张东霞,等. 基于深度强化学习的微电网复合储能协调控制方法[J]. 电网技术, 2019, 043(006): 1914–1921.

ZHANG Zidong, QIU Caiming, ZHANG Dongxia, et al . A coordinated control method for hybrid energy storage system in microgrid based on deep reinforcement learning[J ] . Power System Technology, 2019, 043(006): 1914–1921.

刘静琨, 张宁, 康重庆. 电力系统云储能研究框架与基础模型[J]. 中国电机工程学报, 2017, 37(12): 3361–3371, 3663.

LIU Jingkun, ZHANG Ning, KANG Chongqing. Research framework and basic models for cloud energy storage in power system[J]. Proceedings of the CSEE, 2017, 37(12): 3361–3371, 3663.

匡熠, 王秀丽, 王建学, 等. 基于stackelberg博弈的虚拟电厂能源共享机制[J]. 电网技术, 2020, 445(12): 101–109.

KUANG Yi, WANG Xiuli, WANG Jianxue, et al . Virtual power plant energy sharing mechanism based on stackelberg game[J ] . Power System Technology, 2020, 445(12): 101–109.

慈松. 能量信息化和互联网化管控技术及其在分布式电池储能系统中的应用[J]. 中国电机工程学报, 2015, 35(14): 3643–3648.

CI Song. Energy informatization and internet-based management and its applications in distributed energy storage system[J]. Proceedings of the CSEE, 2015, 35(14): 3643–3648.

WANG J, ZHONG H, QIN J, et al . Incentive mechanism for sharing distributed energy resources[J ] . Journal of Modern Power Systems and Clean Energy, 2019, 7(4): 837–850.

胡鹏, 艾欣, 杨昭, 等. 考虑电能共享的综合能源楼宇群日前协同优化调度[J]. 电力自动化设备, 2019, 39(8): 239–245.

HU Peng, AI Xin, YANG Zhao, et al . Day-ahead optimal scheduling for cluster building with integrated energy system considering power sharing[J ] . Electric Power Automation Equipment, 2019, 39(8): 239–245.

MORSTYN T, HREDZAK B, AGELIDIS V. Cooperative multi-agent control of heterogeneous storage devices distributed in a DC microgrid[J]. IEEE Transactions on Power Systems, 2016, 31(4): 2974–2986.

修晓青, 李建林, 李文启, 等. 储能系统商业模式及其优化规划方法[J]. 电力建设, 2019, 40(6): 41–48.

XIU Xiaoqing, LI Jianlin, LI Wenqi, et al . Research on business model and optimization planning method of energy storage station[J ] . Electric Power Construction, 2019, 40(6): 41–48.

杨永标, 于建成, 李奕杰, 等. 含光伏和蓄能的冷热电联供系统调峰调蓄优化调度[J]. 电力系统自动化, 2017, 41(6): 6–12, 29.

YANG Yongbiao, YU Jiancheng, LI Yijie, et al . Optimal load leveling dispatch of CCHP incorporating photovoltaic and storage[J ] . Automation of Electric Power Systems, 2017, 41(6): 6–12, 29.

张宁, 王毅, 康重庆, 等. 能源互联网中的区块链技术研究框架与典型应用初探[J]. 中国电机工程学报, 2016, 36(15): 4011–4023.

ZHANG Ning, WANG Yi, KANG Chongqing, et al . Blockchain technique in the energy internet: preliminary research framework and typical application[J ] . Proceedings of the CSEE, 2016, 36(15): 4011–4023.

项顶, 宋永华, 胡泽春, 等. 电动汽车参与V2G的最优峰谷电价研究[J]. 中国电机工程学报, 2013, 33(31): 15–25, 2.

XIANG Ding, SONG Yonghua, HU Zechun, et al . Research on optimal time of use price for electric vehicle participating V2G[J ] . Proceedings of the CSEE, 2013, 33(31): 15–25, 2.

孙孝峰, 郝彦丛, 王宝诚, 等. 微电网分布式储能单元荷电状态平衡和电压恢复[J]. 中国电机工程学报, 2016, 36(15): 4047–4055.

SUN Xiaofeng, HAO Yancong, WANG Baocheng, et al . State-of-charge balancing of distributed energy storage units and voltage restoration in microgrid[J ] . Proceedings of the CSEE, 2016, 36(15): 4047–4055.

孙毅, 刘迪, 崔晓昱, 等. 面向居民用户精细化需求响应的等梯度迭代学习激励策略[J]. 电网技术, 2019, 43(10): 3597–3605.

SUN Yi, LIU Di, CUI Xiaoyu, et al . Equal gradient iterative learning incentive strategy for accurate demand response of resident users[J ] . Power System Technology, 2019, 43(10): 3597–3605.

刘国静, 韩学山, 王尚, 等. 基于强化学习方法的风储合作决策[J]. 电网技术, 2016, 40(9): 2729–2736.

LIU Guojing, HAN Xueshan, WANG Shang, et al . Optimal decision-making in the cooperation of wind power and energy storage based on reinforcement learning algorithm[J ] . Power System Technology, 2016, 40(9): 2729–2736.

李正茂, 张峰, 梁军, 等. 含电热联合系统的微电网运行优化[J]. 中国电机工程学报, 2015, 35(14): 3569–3576.

LI Zhengmao, ZHANG Feng, LIANG Jun, et al . Optimization on microgrid with combined heat and power system[J ] . Proceedings of the CSEE, 2015, 35(14): 3569–3576.

栗然, 党磊, 董哲, 等. 分时电价与风储联合调度协调优化的主从博弈模型[J]. 电网技术, 2015, 35(11): 3247–3253.

LI Ran, DANG Lei, DONG Zhe, et al . Coordinated optimization of time-of-use price and dispatching model combining wind power and energy storage under guidance of master-slave game[J ] . Power System Technology, 2015, 35(11): 3247–3253.

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